But even if the company wins that battle, it stands to lose the strategic war.
“It’s really important to coordinate all the bits together so that insights gained in one area can become inputs in another area,” says Melissa Boxer, vice president for Oracle Adaptive Intelligent Applications. “Then, the company will continually deepen and enrich its understanding of the business and can quickly identify opportunities it wouldn’t otherwise see.”
Most companies invest in these new technologies with a functional focus. For instance, a company deploys a new IoT technology on its factory floor to monitor inventory, while its customer support department introduces a machine-learning capability to speed its response times, and improve the accuracy of the information that support reps use. Over in sales, a new application that uses machine learning suggests the next best action to take with each customer, and marketing just added an AI application that delivers personalized offers to customers—based on a range of internal and external data. In finance, a new capability automates a range of processes and sends alerts to managers when it detects anomalies.
While each of these independent technologies is beneficial, they can become truly strategic advantages if the insights they help produce in one area inform other areas through integrated systems and business processes.
Deeper, More Actionable Insights
Even departments within the same functional area have data that, if melded, would provide far more useful insight.
For example, sales has information about a customer’s interests based on recent interactions with a rep. Marketing knows which of the company’s emails the customer has opened in the last few months. Customer service knows which products the customer has had problems with. And the social media team has insight into a customer’s engagement with the company on Twitter.
But their systems are disconnected. Furthermore, all of that information is outdated—that is, it reflects a customer’s past behavior—and it doesn’t include any data from outside the company.
“To get an accurate view of customers, companies need third-party data in addition to their data,” Boxer says, “and all of that needs to be easily consumed by their systems in real time.”
But wait, there’s more. What if the company combines that real-time insight with predictive analytics on data it collects from IoT devices on the factory floor, from logistics, and in the warehouse—as well as historical customer payment information from finance and third-party data on the financial health of that customer? Then that company could:
- Negotiate better contracts
- Enable sales to offer the right products to the right customers at the right time
- Help marketing plan location-based campaigns
- Connect the dots across lines of business to uncover new opportunities or potential problems
“What may look like a problem in one area—for example, when an IoT device detects an oven that’s running too hot in one of your four factories—may actually be a business opportunity that you can see only with data from other areas,” Boxer says. “What if the region where the oven is too hot is seeing a sharp spike in sales because the hotter oven is cracking the baked goods, resulting in frosting working its way down into the cake?”
With that cross-functional insight, the company can identify the possible linkage between that “problem” on the factory floor and the increase in sales, then test the theory that this error is instead an innovation. If that hypothesis proves to be true, the company would know to crank up the other ovens instead of fixing the hotter oven (and being perplexed when sales in that region went back down). And it can quickly design marketing campaigns to promote this happy mistake.
Building the Connections
But a company doesn’t become a “connected enterprise” by accident.
As a company incorporates emerging technologies into its expanding digital business, it must create new incentives that rise above traditional departmental silos.
“The ultimate goal is metrics targeting overall service levels and business growth,” says Roddy Martin, Oracle vice president of product marketing for supply chain. “To get there, leaders must focus on getting beyond greater efficiency in current operations to a new business operating model that integrates supply chain with ERP, marketing, and sales.”
Most companies will struggle with that challenge. One of IDC’s recent predictions is that by 2020, 60% of organizations adopting “intelligent” ERP (enterprise resource planning) applications—that is, those using machine learning and advanced data analytics —“will fail to fully benefit from their intelligent capabilities, unless data and application integration are addressed with an enterprise-wide data strategy.”
In this cloud computing era, in which enterprise application choices increasingly are being made outside of the IT department, companies are realizing a range of short-term wins: faster implementations, more modern functionality and interfaces. But the unintended consequences of selecting applications in this “democratized” manner include creating new information silos and making system, data, and business process integration all that much more complex.
“Companies have access to more data than ever before,” Boxer notes, “but without a holistic strategy and without a focus on choosing and integrating technologies across the organization to achieve that strategy, they will not be able to take full advantage of new technology innovation. They’re likely to have competitors that do.”
